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If you’re up for some Sunday biological geekerys, you might enjoy this video introducing the iDu Optics’ LabCam microscope adapter, that will fit your iPhone into a microscope ocular to show the image on the screen. On my old blog ATCGeek, I wrote a couple of comments on the possible role smartphones might have in biological research, and described some Android apps for genome browsing, or that geeky idea to build a microscope with a smartphone and a couple of pieces of plexiglass. Despite the comment of many is that these devices won’t be of much help in wet and dry labs, we can affirm that they can still provide “a little help” in many situations, such as easying the visualization of a genome sequence when you cannot leave the bench, or just helping in a better and more comfortable visualization of a microscope sample, as in this case.

iDu Optics LabCam Microscope Adapter is mostly designed to work for iPhone, even if a Samsung S6 version is available. You can buy it on the website via PayPal, or on Amazon, but the price is still someway prohibitive. All the models range around 250$. Ok, you might want not to go blind each time you have to view something on a microscope, but maybe this is not worthwile this price, that is the only read drawback I get to see in this amazing product.

Being in science is basically a matter of applying for a position. As you graduate in your master, you swiftly approach your first “application round” for a PhD, and after your PhD, the endless post-doc route will take you into several cover letter writing, interviews and candidate selections. As an high level scientist commented to me once, after that his institute rejected (quite harshly I’d say) my application, it is fundamental to be able to properly attend an interview, because scientific institutions are importing the recruitment practices from companies. But how much are they effective?

In this TED talk, Regina Hartley, New York born Senior Professional in Human Resources (SPHR) from the HRCI, explains us that the right candidate may not be the perfect one. As you screen your candidates profile, one of the things that should matter is how much the person you are analysing demonstrated the capability to sneak out from hard conditions. Thus, an honour graduate in a prestigious university may not be as effective as someone who came out from a public university and had to face several difficulties in his/her life.

Here’s the link to the video in case player above does not work (I am having problems with that, actually).

As we talk about information in biology, mind goes through DNA and protein sequences, on the tracks of what we have learned to call “bioinformatics”, along with a fair amount of algorithms, open source methods, coding hacks, genome assemblies, libraries and bugs. Luckily, Biology is much more complex and beautiful than mere green strings, and information in biological systems flows at different scales, involving several processes. And if we can call communication the information transfer between two entities, the capability of a system to learn from environmental information in order to improve its adaptive response, could be fairly (even if a bit boldly) defined as intelligence.

What is intelligence? Where does it rely? How can we measure it? Great questions, that would generate a huge discussion. Far bigger than this small blog. Anyway, I guess that the best option here is to start talking about something very simple. A slimy mold, for instance.

Physarum polycephalum is known as the many-headed slime and, as reported on Wikipedia is a slime mold that inhabits shady, cool, moist areas, such as decaying leaves and logs. Like slime molds in general, it is sensitive to light; in particular, light can repel the slime mold and be a factor in triggering spore growth. The really amazing fact about this slimy fellow, is that many investigations have proved him as capable of the capability to solve complex problems.

The video I am sharing above these lines, is a TED talk held by Heather Barnett. Designer working with bio-materials and artist, Heather Barnett creates art with slime mold, and shows us how much amazing this organism can be.

With a simple, but very effective cell-based information processing system, P. polycepalum has been proved to quickly find the best path to food through a maze, way faster than me when I had to find the best path to train station from my home through Gràcia. More, the video shows how the mold reconstructed in scale the Tokyo suburban rail system, proving its capability to solve complex problems and tasks.

Of course, to anyone studying cognitive processes at a molecular level, this organism provides an excellent model, but we may also fetch some good idea for evolutionary biology too. The best way to thread into this is a visit to Heather Barnett’s website, that is provided with many video (there is a youtube channel too), information and references.

A “Machine Learning” algorithm is defined as an algorithm able to change its structure and functioning according to the data submitted. In other words, a machine learning algorithm is capable to learn from data and be refined after implementation. Nowadays, many structural biology (e.g. psi-pred, jpred), bioinformatics (HMM-based software) and systems biology (network analysis and db comparison) algorithms rely on machine learning methods, and an insight of the basic principles underlying them is very useful to all those that are working on software development. Unfortunately, an extensive study of such an advanced topic may be pretty tough for someone with a biological background.

Surfing on YouTube, I have been really pleased to find the mathematicalmonk’s channel. Actually, I have no clue on who this guy is, but I am pretty sure that he did a good work with his tutorials. Along with other advanced mathematical topics, Machine Learning is explained in a 160 videos playlist, where the author explains the base concept of Machine Learning with simplicity and great clearness. The course goes through all the major topics needed for an introduction to machine learning methods, and it’s a perfect point to start your exploration in the machine learning.

Above this post, you can play the introductory video, to get an idea about the topic and the kind of lessons proposed. The whole playlist can be found following this link.

I often spend some time on Rafael Irizzarry’s youtube channel, that is provided with very useful and clear video tutorials and insights on bioinformatics and statistics. In this video- tutorial, a farily explanatory introduction to RNA- Seq data analysis is perfromed by Kasper Hansen, who gives an introduction to RNAseq and relevant computational and statistical issues. The tutorial starts explaining the RNA-Seq technical basics, to go further with an insight of the statistical methods of data analysis and some good tip on how to conduct your RNA-Seq analysis routine.

I am making a big use of this, since I am improving my RNA-Seq analysis skills at the moment, and I really hope and think that you will enjoy this talk as I did.

When I started off with coding, I used to joke with the guys at my lab about the possibility to bring bioinformatics on mobile devices. I used to say that my fondest dream was to implement a BLAST application for iPhone. Some time later, someone actually did it on Android, with poor luck. Indeed, mobile devices are quite pointless for bioinfomraitcs, since you cannot really use such a unconfortable mean to do your job, and people would not really bring work on their phones, that are still very linked with private and fun usage. I fairly think that the Steve Job’s last prophecy about the end of the personal computers age will not accomplish for professionals, and that the most of the efforts in bioinformatics software production has to be made in desktop environments.

Someone just doesn’t agree. I have found this paper surfing on twitter, and it worths some lines on this blog, because you have to be a real geek to do stuff like this. DNAApp is a mobile application, available for Android and iOS, and developed in Singapore. Basically, it reads ab1 sequencing files, providing some tools for sequence processing such as reverse complementation, protein translation and searching for specific sequences, with some incorporated functions that would facilitate the harnessing of online Web tools for a full range of analysis.

The guys sharing this on twitter, were actually discussing whether this could be eligible as the most pointless bioinformatics paper in recent years. After a couple of months in working with experimentalists here in Santa Lucia Foundation in Rome, I don’t really agree. Very often, it’s really convenient to share data with people working on a bench, being out for some specific task, or just attending a conference. Experimentalists tend to be a little more “dynamic” than bioinformaticists, and having an application to rapidly view your data and make some quick exploration may be very appreciated.

The intent to provide a tool for bioinformatics analysis may thus fail, but the application may rally some good feedback from those working in wet-lab. Even if this surprised me quite a lot, I would not definetly brand this as totally pointless.

Leopard jacket, a plastic red shirt. A kitsch style that only Brits can wear with unquestionable style. William Latham gains the attention at the first sight. Born in 1961, he pioneered the field of computer art, and got known for his organic artworks based on the processes of evolution. After having founded the Computer Artworks Ltd, game studio that produced the horror videogame “THE THING“, he joined the Goldsmith University of London for a Computer Art professorship. Being involved in a research project where he applies his evolutionary rule-based approach to the domain of protein folding, there is definetly no one better than him to explain the encounter of art and science in the last 30 years.

If you have an hour and half free, you can consider chilling with this amazing lecture, where prof.Latham will go explain the merge of art and science in several fields, such as architecture, dance, rave culture, genetics, creative development in video game and neurosciences.